
import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=sys.maxsize	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					'''bilinear'''
           UpperCamelCase__     : List[str]											=					max_size
           UpperCamelCase__     : str											=					short_edge_length






     def __call__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					[]
           for img in imgs:
                 UpperCamelCase__     : str											=					img.shape[:2]
                 # later: provide list and randomly choose index for resize
                 UpperCamelCase__     : Optional[int]											=					np.random.randint(self.short_edge_length[0]    ,    self.short_edge_length[1] + 1	)
                 if size == 0:
                       return img
                 UpperCamelCase__     : Union[str, Any]											=					size * 1.0 / min(__UpperCAmelCase    ,    __UpperCAmelCase	)
                 if h < w:
                       UpperCamelCase__     : Union[str, Any]											=					size, scale * w
                 else:
                       UpperCamelCase__     : List[Any]											=					scale * h, size
                 if max(__UpperCAmelCase    ,    __UpperCAmelCase	) > self.max_size:
                       UpperCamelCase__     : Optional[Any]											=					self.max_size * 1.0 / max(__UpperCAmelCase    ,    __UpperCAmelCase	)
                       UpperCamelCase__     : Optional[Any]											=					newh * scale
                       UpperCamelCase__     : Optional[int]											=					neww * scale
                 UpperCamelCase__     : List[Any]											=					int(neww + 0.5	)
                 UpperCamelCase__     : Tuple											=					int(newh + 0.5	)

                 if img.dtype == np.uinta:
                       UpperCamelCase__     : List[Any]											=					Image.fromarray(__UpperCAmelCase	)
                       UpperCamelCase__     : Union[str, Any]											=					pil_image.resize((neww, newh)    ,    PILImageResampling.BILINEAR	)
                       UpperCamelCase__     : Union[str, Any]											=					np.asarray(__UpperCAmelCase	)
                 else:
                       UpperCamelCase__     : Dict											=					img.permute(2    ,    0    ,    1	).unsqueeze(0	)  # 3, 0, 1)  # hw(c) -> nchw
                       UpperCamelCase__     : Dict											=					nn.functional.interpolate(
                           __UpperCAmelCase    ,    (newh, neww)    ,    mode=self.interp_method    ,    align_corners=__UpperCAmelCase	).squeeze(0	)
                 img_augs.append(__UpperCAmelCase	)

           return img_augs




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]    ,    cfg.INPUT.MAX_SIZE_TEST	)
           UpperCamelCase__     : Optional[int]											=					cfg.INPUT.FORMAT
           UpperCamelCase__     : Union[str, Any]											=					cfg.SIZE_DIVISIBILITY
           UpperCamelCase__     : List[Any]											=					cfg.PAD_VALUE
           UpperCamelCase__     : str											=					cfg.INPUT.MAX_SIZE_TEST
           UpperCamelCase__     : Optional[int]											=					cfg.MODEL.DEVICE
           UpperCamelCase__     : List[Any]											=					torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)    ,    1    ,    1	)
           UpperCamelCase__     : Optional[Any]											=					torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)    ,    1    ,    1	)
           UpperCamelCase__     : Tuple											=					lambda __SCREAMING_SNAKE_CASE	: (x - self.pixel_mean) / self.pixel_std






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					tuple(max(__UpperCAmelCase	) for s in zip(*[img.shape for img in images]	)	)
           UpperCamelCase__     : Optional[Any]											=					[im.shape[-2:] for im in images]
           UpperCamelCase__     : Optional[Any]											=					[
               nn.functional.pad(
                   __UpperCAmelCase    ,    [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,    value=self.pad_value    ,    )
               for size, im in zip(__UpperCAmelCase    ,    __UpperCAmelCase	)
           ]

           return torch.stack(__UpperCAmelCase	), torch.tensor(__UpperCAmelCase	)






     def __call__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=False	)	-> Tuple:




           """simple docstring"""







           with torch.no_grad():
                 if not isinstance(__UpperCAmelCase    ,    __UpperCAmelCase	):
                       UpperCamelCase__     : List[str]											=					[images]
                 if single_image:
                       assert len(__UpperCAmelCase	) == 1
                 for i in range(len(__UpperCAmelCase	)	):
                       if isinstance(images[i]    ,    torch.Tensor	):
                             images.insert(__UpperCAmelCase    ,    images.pop(__UpperCAmelCase	).to(self.device	).float()	)
                       elif not isinstance(images[i]    ,    torch.Tensor	):
                             images.insert(
                                 __UpperCAmelCase    ,    torch.as_tensor(img_tensorize(images.pop(__UpperCAmelCase	)    ,    input_format=self.input_format	)	)
                                 .to(self.device	)
                                 .float()    ,    )
            # resize smallest edge
                 UpperCamelCase__     : Any											=					torch.tensor([im.shape[:2] for im in images]	)
                 UpperCamelCase__     : Tuple											=					self.aug(__UpperCAmelCase	)
                 # transpose images and convert to torch tensors
                 # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
                 # now normalize before pad to avoid useless arithmetic
                 UpperCamelCase__     : Optional[int]											=					[self.normalizer(__UpperCAmelCase	) for x in images]
                 # now pad them to do the following operations
                 UpperCamelCase__     : List[str]											=					self.pad(__UpperCAmelCase	)
                 # Normalize

                 if self.size_divisibility > 0:
                       raise NotImplementedError()
                 # pad
                 UpperCamelCase__     : Optional[Any]											=					torch.true_divide(__UpperCAmelCase    ,    __UpperCAmelCase	)
                 if single_image:
                       return images[0], sizes[0], scales_yx[0]
                 else:
                       return images, sizes, scales_yx


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      assert torch.isfinite(lowerCamelCase_		).all(), "Box tensor contains infinite or NaN!"
      UpperCamelCase__     : List[Any]											=					box_size
      tensor[:, 0].clamp_(min=0			,		max=lowerCamelCase_		)
      tensor[:, 1].clamp_(min=0			,		max=lowerCamelCase_		)
      tensor[:, 2].clamp_(min=0			,		max=lowerCamelCase_		)
      tensor[:, 3].clamp_(min=0			,		max=lowerCamelCase_		)





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


lowercase			:				Dict   =     logging.get_logger(__name__)

lowercase			:				Dict   =     OrderedDict(
    [
        ("align", "EfficientNetImageProcessor"),
        ("beit", "BeitImageProcessor"),
        ("bit", "BitImageProcessor"),
        ("blip", "BlipImageProcessor"),
        ("blip-2", "BlipImageProcessor"),
        ("bridgetower", "BridgeTowerImageProcessor"),
        ("chinese_clip", "ChineseCLIPImageProcessor"),
        ("clip", "CLIPImageProcessor"),
        ("clipseg", "ViTImageProcessor"),
        ("conditional_detr", "ConditionalDetrImageProcessor"),
        ("convnext", "ConvNextImageProcessor"),
        ("convnextv2", "ConvNextImageProcessor"),
        ("cvt", "ConvNextImageProcessor"),
        ("data2vec-vision", "BeitImageProcessor"),
        ("deformable_detr", "DeformableDetrImageProcessor"),
        ("deit", "DeiTImageProcessor"),
        ("deta", "DetaImageProcessor"),
        ("detr", "DetrImageProcessor"),
        ("dinat", "ViTImageProcessor"),
        ("donut-swin", "DonutImageProcessor"),
        ("dpt", "DPTImageProcessor"),
        ("efficientformer", "EfficientFormerImageProcessor"),
        ("efficientnet", "EfficientNetImageProcessor"),
        ("flava", "FlavaImageProcessor"),
        ("focalnet", "BitImageProcessor"),
        ("git", "CLIPImageProcessor"),
        ("glpn", "GLPNImageProcessor"),
        ("groupvit", "CLIPImageProcessor"),
        ("imagegpt", "ImageGPTImageProcessor"),
        ("instructblip", "BlipImageProcessor"),
        ("layoutlmv2", "LayoutLMv2ImageProcessor"),
        ("layoutlmv3", "LayoutLMv3ImageProcessor"),
        ("levit", "LevitImageProcessor"),
        ("mask2former", "Mask2FormerImageProcessor"),
        ("maskformer", "MaskFormerImageProcessor"),
        ("mgp-str", "ViTImageProcessor"),
        ("mobilenet_v1", "MobileNetV1ImageProcessor"),
        ("mobilenet_v2", "MobileNetV2ImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevitv2", "MobileViTImageProcessor"),
        ("nat", "ViTImageProcessor"),
        ("oneformer", "OneFormerImageProcessor"),
        ("owlvit", "OwlViTImageProcessor"),
        ("perceiver", "PerceiverImageProcessor"),
        ("pix2struct", "Pix2StructImageProcessor"),
        ("poolformer", "PoolFormerImageProcessor"),
        ("regnet", "ConvNextImageProcessor"),
        ("resnet", "ConvNextImageProcessor"),
        ("sam", "SamImageProcessor"),
        ("segformer", "SegformerImageProcessor"),
        ("swiftformer", "ViTImageProcessor"),
        ("swin", "ViTImageProcessor"),
        ("swin2sr", "Swin2SRImageProcessor"),
        ("swinv2", "ViTImageProcessor"),
        ("table-transformer", "DetrImageProcessor"),
        ("timesformer", "VideoMAEImageProcessor"),
        ("tvlt", "TvltImageProcessor"),
        ("upernet", "SegformerImageProcessor"),
        ("van", "ConvNextImageProcessor"),
        ("videomae", "VideoMAEImageProcessor"),
        ("vilt", "ViltImageProcessor"),
        ("vit", "ViTImageProcessor"),
        ("vit_hybrid", "ViTHybridImageProcessor"),
        ("vit_mae", "ViTImageProcessor"),
        ("vit_msn", "ViTImageProcessor"),
        ("xclip", "CLIPImageProcessor"),
        ("yolos", "YolosImageProcessor"),
    ]
)

lowercase			:				Tuple   =     _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def        snake_case__		(    lowerCamelCase_		):
							for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					A      :					Optional[Any]      		=					model_type_to_module_name(lowerCamelCase_		)

																					A      :					Optional[int]      		=					importlib.import_module(F'.{module_name}'     ,				'''transformers.models'''		)
																					try:
																												return getattr(lowerCamelCase_     ,				lowerCamelCase_		)
																					except AttributeError:
																												continue

							for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
														if getattr(lowerCamelCase_     ,				'''__name__'''     ,				lowerCamelCase_		) == class_name:
																					return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
							A      :					Any      		=					importlib.import_module('''transformers'''		)
							if hasattr(lowerCamelCase_     ,				lowerCamelCase_		):
														return getattr(lowerCamelCase_     ,				lowerCamelCase_		)

							return None






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_ = None     ,				lowerCamelCase_ = False     ,				lowerCamelCase_ = False     ,				lowerCamelCase_ = None     ,				lowerCamelCase_ = None     ,				lowerCamelCase_ = None     ,				lowerCamelCase_ = False     ,				**lowerCamelCase_     ,				):
							A      :					Optional[Any]      		=					get_file_from_repo(
							    lowerCamelCase_     ,				lowerCamelCase_     ,				cache_dir=lowerCamelCase_     ,				force_download=lowerCamelCase_     ,				resume_download=lowerCamelCase_     ,				proxies=lowerCamelCase_     ,				use_auth_token=lowerCamelCase_     ,				revision=lowerCamelCase_     ,				local_files_only=lowerCamelCase_     ,				)
							if resolved_config_file is None:
														logger.info(
														    '''Could not locate the image processor configuration file, will try to use the model config instead.'''		)
														return {}

							with open(lowerCamelCase_     ,				encoding='''utf-8'''		) as reader:
														return json.load(lowerCamelCase_		)







class 					__lowercase  :





							"""simple docstring"""

							def __init__(					self						)		->       int:
														raise EnvironmentError(
														    '''AutoImageProcessor is designed to be instantiated '''
														    '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''						)




							@classmethod
							@replace_list_option_in_docstrings(__UpperCAmelCase						)
							def       snake_case			(					cls    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Tuple:
														A      :					Tuple      		=					kwargs.pop('''config'''    ,						__UpperCAmelCase						)
														A      :					Union[str, Any]      		=					kwargs.pop('''trust_remote_code'''    ,						__UpperCAmelCase						)
														A      :					str      		=					True

														A		,		A      :					str      		=					ImageProcessingMixin.get_image_processor_dict(__UpperCAmelCase    ,						**__UpperCAmelCase						)
														A      :					List[Any]      		=					config_dict.get('''image_processor_type'''    ,						__UpperCAmelCase						)
														A      :					List[Any]      		=					None
														if "AutoImageProcessor" in config_dict.get('''auto_map'''    ,						{}						):
																					A      :					List[str]      		=					config_dict['''auto_map''']['''AutoImageProcessor''']

														# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
														# and if so, infer the image processor class from there.
														if image_processor_class is None and image_processor_auto_map is None:
																					A      :					List[str]      		=					config_dict.pop('''feature_extractor_type'''    ,						__UpperCAmelCase						)
																					if feature_extractor_class is not None:
																												logger.warning(
																												    '''Could not find image processor class in the image processor config or the model config. Loading'''
																												    ''' based on pattern matching with the model\'s feature extractor configuration.'''						)
																												A      :					Tuple      		=					feature_extractor_class.replace('''FeatureExtractor'''    ,						'''ImageProcessor'''						)
																					if "AutoFeatureExtractor" in config_dict.get('''auto_map'''    ,						{}						):
																												A      :					Any      		=					config_dict['''auto_map''']['''AutoFeatureExtractor''']
																												A      :					int      		=					feature_extractor_auto_map.replace('''FeatureExtractor'''    ,						'''ImageProcessor'''						)
																												logger.warning(
																												    '''Could not find image processor auto map in the image processor config or the model config.'''
																												    ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''						)

        # If we don't find the image processor class in the image processor config, let's try the model config.
														if image_processor_class is None and image_processor_auto_map is None:
																					if not isinstance(__UpperCAmelCase    ,						__UpperCAmelCase						):
																												A      :					List[str]      		=					AutoConfig.from_pretrained(__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					# It could be in `config.image_processor_type``
																					A      :					Any      		=					getattr(__UpperCAmelCase    ,						'''image_processor_type'''    ,						__UpperCAmelCase						)
																					if hasattr(__UpperCAmelCase    ,						'''auto_map'''						) and "AutoImageProcessor" in config.auto_map:
																												A      :					List[Any]      		=					config.auto_map['''AutoImageProcessor''']

														if image_processor_class is not None:
																					A      :					str      		=					image_processor_class_from_name(__UpperCAmelCase						)

														A      :					int      		=					image_processor_auto_map is not None
														A      :					List[str]      		=					image_processor_class is not None or type(__UpperCAmelCase						) in IMAGE_PROCESSOR_MAPPING
														A      :					Optional[Any]      		=					resolve_trust_remote_code(
														    __UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase						)

														if has_remote_code and trust_remote_code:
																					A      :					Tuple      		=					get_class_from_dynamic_module(
																					    __UpperCAmelCase    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					A      :					Optional[Any]      		=					kwargs.pop('''code_revision'''    ,						__UpperCAmelCase						)
																					if os.path.isdir(__UpperCAmelCase						):
																												image_processor_class.register_for_auto_class()
																					return image_processor_class.from_dict(__UpperCAmelCase    ,						**__UpperCAmelCase						)
														elif image_processor_class is not None:
																					return image_processor_class.from_dict(__UpperCAmelCase    ,						**__UpperCAmelCase						)
														# Last try: we use the IMAGE_PROCESSOR_MAPPING.
														elif type(__UpperCAmelCase						) in IMAGE_PROCESSOR_MAPPING:
																					A      :					Tuple      		=					IMAGE_PROCESSOR_MAPPING[type(__UpperCAmelCase						)]
																					return image_processor_class.from_dict(__UpperCAmelCase    ,						**__UpperCAmelCase						)

														raise ValueError(
														    f'Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '
														    f'`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '
														    f'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()						)}'						)






							@staticmethod
							def       snake_case			(					__UpperCAmelCase    ,						__UpperCAmelCase						)		->       Any:
														IMAGE_PROCESSOR_MAPPING.register(__UpperCAmelCase    ,						__UpperCAmelCase						)






